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检索条件"主题词=detection algorithms"

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Identification Method of Electric Meter Reading Based on YOLOv7 and DCNN

Identification Method of Electric Meter Reading Based on YOL...

引用

Image Processing (ASIP), Asia Symposium on

作者： Mu Changping Geng Liqing Yang Genghuang Zhou Peng Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350376159

ISBN: (纸本)9798350376166

Meter reading recognition is an essential indispensable part of intelligent grid inspection systems. However, due to limitations in the installation angles of monitoring cameras, captured images often suffer from angle issues, thereby reducing recognition accuracy. Therefore, this paper proposes a two-stage detection and recognition algorithm based on YOLOv7-tiny and DCNN networks. Firstly, the key point detection algorithm of YOLOv7 is improved by adding the EVC module, which enables the model to extract image features more comprehensively. Utilizing the Wingloss loss function, key point detection of meter reading regions is achieved. Then, perspective transformation is performed on the meter reading regions based on key point coordinate information to correct the images. Secondly, the deep convolutional neural network (DCNN-CTC) algorithm is employed for text recognition in images. Through extensive experiments with actual meter data, the results indicate that compared to YOLOv5 and CRNN methods, the proposed approach can recognize various meter reading information from different shooting angles with an increased accuracy rate of 5.8%.

关键词： Meters YOLO Accuracy Image recognition Text recognition Inspection Feature extraction Meter reading detection algorithms Monitoring

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Estimation of rotation angle and anisotropic scaling rate using pilot signals for watermarking

Estimation of rotation angle and anisotropic scaling rate us...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Rinka Kawano Masaki Kawamura Graduate School of Sciences and Technology for Innovation Yamaguchi University

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

We propose a method for simultaneously estimating the rotation angle and anisotropic scaling rates. Conventional watermarking methods are robust against non-geometric attacks. However, these methods need to synchronize to resist geometric attacks. Invariant features, such as SIFT, have been used for the synchronization. In this paper, we focus on pilot signals for estimating geometric attacks. By embedding a grid-shaped pilot signal and detecting its change, individual attacks on the scaling rate and rotation angle can be estimated. However, it has been difficult to estimate combined attacks. Our proposed method for estimating rotation angle and anisotropic scaling rates uses Radon coefficients. A Radon transform is applied to the signal obtained by QIM from a stego image. Periodic strong peaks appear in the coefficients at rotation angles. The period of the peaks is also calculated. From the rotation angle and the period at which the peaks appear, the rotation angle and the anisotropic scaling rates can be estimated. Computational results show that the rotation angle can be successfully estimated with an absolute error below 0.2 degrees. In more than half of the results, the absolute error of the anisotropic scaling rates was almost zero.

关键词： Accuracy Radon Estimation Watermarking Transforms Resists Information processing Anisotropic Synchronization detection algorithms

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Scene change detection based on sequence statistics using structural similarity

Scene change detection based on sequence statistics using st...

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Jun Shen Xi Jiang Jinshan Zhong Shimeng Yao School of Mathematics Sichuan University Chengdu China

With the development of the short video, video processing technology is becoming more and more important, among which video scene change detection is an important basic technology. However, due to the inconspicuous feature of scene gradual change, the current algorithm cannot detect it very well. Based on this, we proposed a new scene change detection algorithm. The new algorithm is designed based on the structural similarity (SSIM) algorithm, an index to measure the similarity of two images, and the changing law of structural similarity sequence during scene change. Meantime, we designed a new measurement based on sequence statistics to measure the variation law of SSIM sequences. Experiments show that the algorithm has the characteristics of anti-interference and anti-noise, and it can well solve the above difficulty.

关键词： Technological innovation Fluctuations Change detection algorithms Current measurement Feature extraction Indexes detection algorithms

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YOLOv5 Improved Smoke detection Algorithm *

YOLOv5 Improved Smoke Detection Algorithm *

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Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC), International Conference on

作者： Mixue Zhu Zhiqiang Liu Xu Zhang Wenjing Li Jiaxin Su College of Information Engineering Inner Mongolia University of Technology Hohhot Inner Mongolia College of Construction Technology Hohhot City Inner Mongolia Autonomous Region

The impact of fire on natural resources and the ecological environment is reflected in the fact that it alters the global carbon cycle, vegetation renewal process and soil properties through a combination of natural and human factors, thereby aggravating climate change, and also seriously endangers public life and property and causes huge economic losses. In the early stage of fire, smoke is more obvious than flame, so accurate and fast smoke detection algorithm is of great significance for preventing fire. In order to meet the requirements of smoke detection accuracy and detection speed, an improved real-time smoke detection algorithm is proposed on the basis of YOLOv5. Based on the YOLOv5 network structure, the GhostNet lightweight network is used to replace the backbone network, and the prediction header is replaced by C3Ghost. The C3 module only uses a small amount of traditional convolution to generate some feature maps, which greatly reduces the network parameters and speeds up the network inference speed; the CA attention mechanism is added to the backbone network, which makes the mobile network by embedding position information into the channel attention. Get information on a larger area and avoid introducing large overhead. Compared with the original YOLOv5 network model, the average detection accuracy of the improved method is increased by 2.2%, the FPS is changed from 181.82 to 222.22 frames per second, and the number of model parameters is reduced by 51.3%. The experimental results show that this method can effectively improve the performance of the smoke detection model.

关键词： Climate change Deep learning Fires Safety Alarm systems Image processing Natural resources Biological system modeling detection algorithms Vegetation mapping Real-time systems Human factors

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Semi-Supervised Far-Field Speaker Verification with Distance Metric Domain Adaptation

Semi-Supervised Far-Field Speaker Verification with Distance...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Han Wang Mingrui He Mingjun Zhang Longting Xu School of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Achieving high performance in far-field speaker verification is challenging due to the scarcity of data and prevalence of low-quality labels. A common approach is to use a semi-supervised learning framework. A pre-trained near-field speaker model extracts embeddings of unlabeled far-field speakers, and clustering methods generate pseudo-labels for fine-tuning. The accuracy of pseudo-label generation through clustering and the domain adaptation during fine-tuning are critical to this semi-supervised learning framework. In this paper, we propose a method that combines Uniform Manifold Approximation and Projection (UMAP) with a community detection algorithm for efficient dimensionality reduction and clustering. UMAP preserves the structural distribution of high-dimensional data, facilitating better clustering. We conduct domain alignment in the distance metric space to achieve domain adaptation during fine-tuning, referring to this approach as distance metric domain adaptation (DMDA). We evaluate the effectiveness of the dimensionality reduction community detection agorithm and the on VoxCeleb (pre-trained) and FFSVC (fine-tuned) datasets. Experimental results demonstrate that the accuracy of generating pseudo labels is improved by using the reduced dimensional community detection algorithm. Additionally, the DMDA method effectively reduces domain mismatch issues during the fine-tuning process.

关键词： Measurement Dimensionality reduction Manifolds Accuracy High dimensional data Clustering methods Asia Information processing Semisupervised learning detection algorithms

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Impact of Device Placement on Non-Contact Respiratory Monitoring Using Channel State Information

Impact of Device Placement on Non-Contact Respiratory Monito...

引用

IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Yanliang Jin Chenggang Shao Yanyi Wang Bintao Hu Haonan Hu Yuan Gao School of Communication & Information Engineering Shanghai University Shanghai China School of Internet of Things Xi'an Jiaotong-Liverpool University Suzhou China Department of Electronic and Electrical Engineering The University of Sheffield Sheffield UK

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

With the rapid development of wireless communication technology, health monitoring using commercial wireless devices has become a research hotspot. This paper proposes a contactless respiratory monitoring technology based on channel state information (CSI), which achieves flexible collection of wireless signals through a software defined radio (SDR) platform. A new respiratory frequency detection algorithm is developed using the differential threshold method (DTM), which reduced the complexity of the algorithm and improved the processing efficiency. This study systematically evaluates the impact of device location on signal quality and breath detection accuracy by changing the location of wireless devices in various experimental environments. Experimental results show that this technology can effectively monitor respiratory frequency and is highly robust to the specific placement of the device.

关键词： Wireless communication Wireless sensor networks Software algorithms Signal processing algorithms Signal processing Sensors detection algorithms Monitoring Channel state information Software radio

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WaShadow: Effectively Protecting WebAssembly Memory Through Virtual Machine-Aware Shadow Memory

WaShadow: Effectively Protecting WebAssembly Memory Through ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhuochen Jiang Baojian Hua School of Software Engineering Suzhou Institute for Advanced Research University of Science and Technology of China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

WebAssembly (Wasm) is an emerging binary instruction set architecture designed for secure binary program execution and is rapidly being deployed across various security-critical domains, such as edge computing and smart contracts. However, despite its security-oriented design, Wasm remains susceptible to vulnerabilities, including integer overflows and memory corruption, due to the lack of effective protection mechanisms, which undermines its security *** this paper, we present WaShadow, the first approach for effective Wasm protection using virtual machine-aware shadow memory. Our key insight is that, since Wasm is a virtual instruction set, we can leverage memory layout information in the underlying Wasm VMs to enforce the protection. Specifically, we first extend the Wasm VMs with shadow memory to record memory information and track the status of linear memory, by introducing two new pseudo Wasm instructions for inserting and performing sanity checks on canaries in the linear memory. We then design a static binary instrumentation method to instrument Wasm binaries with canary instructions. Finally, we implement these canary pseudo-instructions through virtual machine extensions as well as a set of vulnerability detection algorithms as security plugins. We implemented a software prototype for WaShadow and conducted extensive experiments to evaluate its effectiveness, usability, and overhead on micro and real-world benchmarks. Experimental results demonstrated that WaShadow is effective in protecting Wasm linear memory against various memory vulnerabilities, with an average code size increase of 26.5% and an execution time penalty of 108.5%.

关键词： Instruments Instruction sets Layout Prototypes Virtual machines Software Security Protection detection algorithms Usability

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An Improved Label Propagation Algorithm for Undirected Weighted Networks

An Improved Label Propagation Algorithm for Undirected Weigh...

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Intelligent Computing and Wireless Optical Communications (ICWOC), International Conference on

作者： Peilei Li Hanqing Yu Yijin Zhang School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350374377

ISBN: (纸本)9798350374384

In recent years, with the rapid development of network technology, the scale of social networks has become increasingly large and diversified. The detection of community has become a hot spot in the research of social network. Label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity. However, the randomness of LPA affects the accuracy and stability of the community. This paper proposes an improved label propagation algorithm for undirected weighted networks. In the first stage, considering both node importance and local topological information, an improved LPA is designed to divide the undirected weighted network into initial communities, thereby reducing the instability of LPA. In the second stage, a strategy for increasing modularity is designed to merge small communities, thereby improving the quality of community partitioning. Experimental results show that the proposed community detection algorithm has better community detection quality compared to LPA and four classical community detection algorithms.

关键词： Wireless communication Accuracy Social networking (online) Optical fiber communication Partitioning algorithms detection algorithms Time complexity

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Workpiece Recognition and Localization Based on Improved YOLOv7-Tiny Channel Pruning

Workpiece Recognition and Localization Based on Improved YOL...

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Robotics, Control and Automation Engineering (RCAE), International Conference on

作者： Mingyao Teng Guoyang Wan Shoujun Bai Xuna Li Chengwen Wang Hanqi Li School of Electrical Engineering Anhui Polytechnic University Wuhu China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

At present, to address the problem of difficult detection of industrial parts and complex model with large computation, a workpiece recognition and detection algorithm based on improved YOLOv7-Tiny channel pruning is proposed. Firstly, the algorithm integrates the ECA module, an efficient channel attention mechanism, into the backbone network to better retain the information in transmission, so that the data features are more fully retained in the process of transmission, and thus the model can be optimized and the detection accuracy of the model can be improved; secondly, the lightweight up-sampling operator CARAFE is invoked to replace the nearest-neighbor difference of the original up-sampling in the structure of the PAFPN in order to improve the feature fusion ability of the model; finally, the improved model is subjected to channel pruning to reduce the number of parameters and computation of the model to further optimize the model. The experimental results show that our method achieves 81.5% recognition accuracy on the self-constructed dataset, and the number of parameters and computation amount decrease by 75.6% and 74.0%, respectively. The effectiveness of the method is proved.

关键词： Location awareness Accuracy Attention mechanisms Service robots Computational modeling Inspection Feature extraction Data models Computational efficiency detection algorithms

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Automated Transformer Bund Wall Drainage System to Mitigate Climate Change Risks

Automated Transformer Bund Wall Drainage System to Mitigate ...

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Public Safety Technology (WFPST), IEEE World Forum on

作者： Mohamed Fayaz Khan Preneesa Govender Andrew Graham Swanson Discipline of Electrical Electronic and Computer Engineering University of KwaZulu Natal Durban South Africa

ISBN: (数字)9798350329148

ISBN: (纸本)9798350329155

In the age of the digital transformation and all the potential benefits and modernized proposals that it promises, it is important for power utilities to begin implementation of these proposed benefits rather than wait for a ‘step-change’ that may never come. To clarify this, an automated transformer bund wall drainage system has been proposed in this paper. This system would replace the manual intervention that is presently required to periodically drain the transformer bund of accumulated rainwater. The proposed Internet of Things (IoT) based system would be required to autonomously detect the level of rainwater, drain it when required to ensure adequate containment of transformer oil in the event of a leak and also to ensure that transformer oil would not release into the environment. With the anticipated increase in precipitation due to climate change this system would eliminate the increased environmental risk and also save maintenance costs by reducing the need for human intervention and labour.

关键词： Digital transformation Maintenance engineering Precipitation Water resources Oil insulation Rain Internet of Things Climate change Environmental monitoring Risk management Autonomous systems detection algorithms Risk management

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